Customer experience starts with understanding what customers want. Multidomain master data management (Multidomain MDM) is a key discipline to help in the successful development of a personalization strategy. Multidomain master data management can help you build a 360° view of the customer that is needed to fuel personalization and hyper-personalization strategies with the right insights.
Hyper-personalization is a marketing strategy that involves tailoring content, products or services to the specific needs and preferences of individual customers using data and artificial intelligence (AI) technology to create a highly personalized experience.
This approach goes beyond traditional personalization techniques, which typically involve segmenting customers into broad groups based on demographics or behavior and instead focuses on understanding each customer's unique characteristics and delivering highly customized experiences at every touchpoint.
Hyper-personalization leverages data such as purchase history, browsing behavior, social media activity and other customer interactions to create a comprehensive profile of each individual customer. AI algorithms then use this data to deliver personalized recommendations, offers and content in real-time across multiple channels, such as email, social media, websites and mobile apps.
The goal of hyper-personalization is to create a seamless and engaging customer experience that increases customer loyalty, improves retention and drives revenue growth for businesses.
Definition on hyper-personalized customer experiences: A customer is able to use technologies to find the exact product or service they are looking for, while also having a personalized experience that reflects their specific interests and preferences throughout the buyers journey.
Hyper-personalization is different from traditional personalization in several key ways:
Scope: Traditional personalization typically involves segmenting customers into broad groups based on demographics or behavior, whereas hyper-personalization involves tailoring content, products or services to the specific needs and preferences of individual customers.
Data usage: Hyper-personalization leverages a wider range of data sources than traditional personalization, such as purchase history, browsing behavior, social media activity and other customer interactions, to create a comprehensive profile of each individual customer.
Real-time delivery: Hyper-personalization uses AI algorithms to deliver personalized recommendations, offers and content in real-time across multiple channels, such as email, social media, websites and mobile apps, whereas traditional personalization may rely on static rules-based approaches that are not as responsive to customer needs.
Granularity: Hyper-personalization is more granular than traditional personalization, focusing on delivering highly customized experiences at every touchpoint, whereas traditional personalization may be limited to a few basic personalization elements, such as product recommendations or personalized greetings.
Overall, hyper-personalization is a more sophisticated and targeted approach to personalization that uses data and AI technology to deliver highly personalized experiences at every touchpoint, with the goal of increasing customer loyalty, improving retention, and driving revenue growth for businesses.
A winning hyper-personalization strategy includes several key elements:
Data collection: To implement hyper-personalization, businesses need to collect and analyze large amounts of data from various sources, including customer interactions, browsing behavior, social media activity and other relevant data points. This data should be collected ethically and in compliance with data protection regulations.
Customer segmentation: Hyper-personalization requires businesses to segment their customers into smaller groups based on shared characteristics or behaviors. This helps create a more personalized experience for each customer and enables businesses to deliver more relevant content, products and services.
Personalization algorithms: Advanced machine learning and AI algorithms are needed to process the vast amounts of data collected and deliver personalized recommendations, offers and content in real-time across multiple channels. These algorithms should be regularly refined and optimized to improve accuracy and relevance.
Multichannel approach: A winning hyper-personalization strategy involves delivering personalized experiences across multiple touchpoints, such as email, social media, websites and mobile apps. This enables businesses to meet customers where they are and provide a seamless and consistent experience across channels.
Testing and optimization: Hyper-personalization is an ongoing process that requires continuous testing and optimization to improve effectiveness and ensure that the strategy is aligned with changing customer needs and preferences. Businesses should regularly analyze data and performance metrics and make adjustments to their strategy accordingly.
Privacy and security: As hyper-personalization involves collecting and analyzing large amounts of customer data, businesses must prioritize data privacy and security to maintain customer trust and comply with data protection regulations.
Overall, a winning hyper-personalization strategy requires businesses to collect and analyze customer data ethically, segment customers into smaller groups, use advanced algorithms to deliver personalized experiences across multiple channels and continuously test and optimize their strategy.
Hyper-personalization offers several key benefits for businesses, including:
By tailoring content, products and services to the specific needs and preferences of individual customers, hyper-personalization creates a more engaging and personalized experience that can improve customer satisfaction and loyalty.
Hyper-personalization can help businesses build stronger relationships with customers by delivering relevant and personalized experiences that keep customers coming back.
Personalized recommendations, offers and content can help drive conversions by making it easier for customers to find products or services that meet their needs and preferences.
By delivering more targeted and relevant experiences, hyper-personalization can help businesses maximize the return on their marketing and advertising investments.
By delivering personalized experiences that meet customers' needs and preferences, businesses can build a stronger brand reputation and differentiate themselves from competitors.
Hyper-personalization requires collecting and analyzing large amounts of customer data, which can provide businesses with valuable insights into customer behavior and preferences that can inform future marketing and business strategies.
Overall, hyper-personalization can help businesses improve the customer experience, drive customer loyalty and retention, increase conversions and ROI and gain deeper customer insights that can inform future business decisions.
Let’s take a closer look at some of the typical data management challenges that prevent you from achieving personalization and what role multidomain master data management can play to alleviate that.
Multidomain master data management provides the governance capability to support business ownership and stewardship of the key data elements that are needed for personalization, including customer data, product data, channel data, location data, and more.
Multidomain master data management can help through its data governance capabilities to ensure that master data such as product and customer data is accurate, complete, and coherent.
Multidomain master data management does not develop data on its own. However, it can harness small data that, when combined with master data, yields new insights. For example, understanding that two people share the same address may reveal a household relationship.
The ability to implement a single point of data governance in the organization means that new data sources may be onboarded more easily. For example, census data and social media data, while broad in what content they carry on their own, when combined correctly with master data records, can reveal new insight.
The ability to drive analytics dynamically at the point of customer interaction that also takes into account the customer behavior is a key personalization capability. Whether this is done by the recommendation engine directly or in conjunction with a customer services agent, the results often yield key decisions – for example, next-best offer or privacy consent – that are immediately relevant to other channels, points of interaction, analytics, and operational systems. Master data management provides an ideal repository for the collection and sharing of these “small data” elements in order to help create a coherent and customer-centric experience.
This challenge has three different root causes: 1) the customer does not give consent to use the information, 2) the tools to collect the information are lacking or insufficient, and 3) the management discipline and organization required to coordinate data objectives is ill-defined.
Multidomain master data management can help to act as a data brokerage in the support of these three challenges. It brings discipline to data management and provides a safe place to keep the collated data. In so far as asking the customer to share their data, this should be promoted in the development of a win-win scenario where the customer gets clear benefits from doing so without compromising their preferences for privacy. Master data management can ensure that this process is transparent, by recording, governing, and auditing the consent and the consented data.
“…most marketers are missing opportunities in using a broader set of customer data to improve personalization efforts…“ - Gartner Magic Quadrant for Personalization Engines, 2020
Personalization typically sits beyond more traditional mass-market segmentation strategies and is often characterized as targeting the individual or a collection of individuals with very similar characteristics, interests, and problems, for example, medical professionals or vehicle owners. Segmentation generally addresses a much wider set of demographic similarities, such as geographical region, or gender.
While macro and micro-segmentation strategies remain important, they tend to rely on a view of the world more from the vendor’s perspective and not necessarily that of the consumer. Invariably this leads to a reactive posture; looking at what a consumer has done rather than looking at what they are doing or anticipating their motives.
To differentiate in customer experience, personalization is key. Personalization automatically changes the way the service is being delivered, depending on how it is being used and perceived. For example, when visiting the website of the BBC for the latest news, there may also be displayed some local news, in France for example, based on your current location. In another example, eCommerce sites will often display product recommendations based on previous purchase history.
Recommendation engines are tools that adjust the experience on a website according to behavior. These engines will often have learning algorithms that will change search results, predict product interest, and change the order of pages to automate recommendation strategies.
Personalization is a highly data-intensive process. The accuracy, relevance, coherence, timeliness, pertinence, and intimacy of the data being used to support personalization is going to have a direct impact on your customer’s experience. Do you want to see dog food being recommended when you own a cat?
65% of companies consider the management, quality and availability of data as a major inhibitor of personalization. - Boston Consulting Group, 2019
Achieving successful personalization is challenging. Despite some good tools being available, such as dedicated recommendation engine solutions, for many, they are only as good as the data that drives them.
That’s why you need multidomain master data management.
Beyond single domain governance, multidomain master data governance adds new capabilities that play important roles in the management of the data that specifically supports hyper-personalization. Note that multiple-domain refers to a collection of single domain governance capabilities and is not to be confused with multidomain which refers to a unique, cross-domain governance capability. Multidomain master data management carries zones of insight.
Zones of insight refer to the data management capabilities that are unique to cross-domain governance and that make hyper-personalization effective.
Zones of insight provide supporting data management capabilities for hyper-personalization and drive new business models.
The zones of insight occur in the intersections of data domains, here Customer, Product, Location, and Supplier:
There is no getting around it. Successful hyper-personalization relies on hard-to-get and hard-to-manage data. Multidomain master data management can help by providing data collection and management capabilities.
The types of data that might be considered to be managed within a multidomain master data management solution to support hyper-personalization include:
As the list indicates, a truly 360° view of the customer is comprehensive and encompasses not just customer data, but also the context of the customer.
Multidomain master data management, via its ability to manage zones of insight, can not only help to see how a product is sold but also potentially, help to provide insight into how it is used.
Only multidomain master data management will support hyper-personalization.
Multidomain MDM provides governance capabilities that are often used to build a 360° customer view. For hyper-personalization, two types of data sets are particularly useful from this 360° customer view: relationships and insights.
Relationships may be inter- or cross-domain. For example, when defining what constitutes a household, multiple parties and location data must be governed together. A household is not necessarily defined in the same way for all organizations. It might be address-based, or more family member-oriented, for example. Multidomain MDM helps to determine and align the data so that it correctly reflects the policy of household definition.
Insights are arguably the golden data elements of hyper-personalization. They help to make the user experience unique. Many of the data elements can be considered as being small data, such as determining location-based preferences, sentimental analysis, or a life event.